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bias kernel psi stats implemented.

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This commit is contained in:
James Hensman 2012-11-30 15:49:20 +00:00
parent cd22c96e00
commit 6d0a7afd4c
6 changed files with 170 additions and 113 deletions
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37
GPy/kern/bias.py
View file

@ -46,3 +46,40 @@ class bias(kernpart):
def dKdiag_dX(self,partial,X,target): def dKdiag_dX(self,partial,X,target):
pass pass
def psi0(self, Z, mu, S, target):
target += self.variance
def psi1(self, Z, mu, S, target):
target += self.variance
def psi2(self, Z, mu, S, target):
target += self.variance**2
def dpsi0_dtheta(self, partial, Z, mu, S, target):
target += partial.sum()
def dpsi0_dZ(self, partial, Z, mu, S, target):
pass
def dpsi0_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass
def dpsi1_dtheta(self, partial, Z, mu, S, target):
target += partial.sum()
def dpsi1_dZ(self, partial, Z, mu, S, target):
pass
def dpsi1_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass
def dpsi2_dtheta(self, partial, Z, mu, S, target):
target += 2.*self.variance*partial.sum()
def dpsi2_dZ(self, partial, Z, mu, S, target):
pass
def dpsi2_dmuS(self, partial, Z, mu, S, target_mu, target_S):
pass

72
GPy/kern/kern.py
View file

@ -201,70 +201,80 @@ class kern(parameterised):
[p.dKdiag_dX(partial[s],X[s,i_s],target[s,i_s]) for p,i_s,s in zip(self.parts,self.input_slices,slices)] [p.dKdiag_dX(partial[s],X[s,i_s],target[s,i_s]) for p,i_s,s in zip(self.parts,self.input_slices,slices)]
return target return target
def psi0(self,Z,mu,S,slices_mu=None,slices_Z=None): def psi0(self,Z,mu,S,slices=None):
slices = self._process_slices(slices,False)
target = np.zeros(mu.shape[0]) target = np.zeros(mu.shape[0])
[p.psi0(Z,mu,S,target) for p in self.parts] [p.psi0(Z,mu[s],S[s],target[s]) for p,s in zip(self.parts,slices)]
return target return target
def dpsi0_dtheta(self,Z,mu,S): def dpsi0_dtheta(self,partial,Z,mu,S,slices=None):
target = np.zeros((mu.shape[0],self.Nparam)) slices = self._process_slices(slices,False)
[p.dpsi0_dtheta(Z,mu,S,target[s]) for p,s in zip(self.parts, self.param_slices)] target = np.zeros(self.Nparam)
[p.dpsi0_dtheta(partial[s],Z,mu[s],S[s],target[ps]) for p,ps,s in zip(self.parts, self.param_slices,slices)]
return target return target
def dpsi0_dmuS(self,Z,mu,S): def dpsi0_dmuS(self,partial,Z,mu,S,slices=None):
slices = self._process_slices(slices,False)
target_mu,target_S = np.zeros_like(mu),np.zeros_like(S) target_mu,target_S = np.zeros_like(mu),np.zeros_like(S)
[p.dpsi0_dmuS(Z,mu,S,target_mu,target_S) for p in self.parts] [p.dpsi0_dmuS(partial,Z,mu[s],S[s],target_mu[s],target_S[s]) for p,s in zip(self.parts,slices)]
return target_mu,target_S return target_mu,target_S
def psi1(self,Z,mu,S): def psi1(self,Z,mu,S,slices1=None,slices2=None):
"""Think N,M,Q """ """Think N,M,Q """
slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros((mu.shape[0],Z.shape[0])) target = np.zeros((mu.shape[0],Z.shape[0]))
[p.psi1(Z,mu,S,target=target) for p in self.parts] [p.psi1(Z[s2],mu[s1],S[s1],target[s1,s2]) for p,s1,s2 in zip(self.parts,slices1,slices2)]
return target return target
def dpsi1_dtheta(self,Z,mu,S): def dpsi1_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
"""N,M,(Ntheta)""" """N,M,(Ntheta)"""
target = np.zeros((mu.shape[0],Z.shape[0],self.Nparam)) slices1, slices2 = self._process_slices(slices1,slices2)
[p.dpsi1_dtheta(Z,mu,S,target[:,:,s]) for p,s in zip(self.parts, self.param_slices)] target = np.zeros((self.Nparam))
[p.dpsi1_dtheta(partial[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,ps,s1,s2,i_s in zip(self.parts, self.param_slices,slices1,slices2,self.input_slices)]
return target return target
def dpsi1_dZ(self,Z,mu,S): def dpsi1_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
"""N,M,Q""" """N,M,Q"""
target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[1])) slices1, slices2 = self._process_slices(slices1,slices2)
[p.dpsi1_dZ(Z,mu,S,target) for p in self.parts] target = np.zeros_like(Z)
[p.dpsi1_dZ(partial[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target return target
def dpsi1_dmuS(self,Z,mu,S): def dpsi1_dmuS(self,partial,Z,mu,S,slices1=None,slices2=None):
"""return shapes are N,M,Q""" """return shapes are N,M,Q"""
target_mu, target_S = np.zeros((2,mu.shape[0],Z.shape[0],Z.shape[1])) slices1, slices2 = self._process_slices(slices1,slices2)
[p.dpsi1_dmuS(Z,mu,S,target_mu=target_mu,target_S = target_S) for p in self.parts] target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
[p.dpsi1_dmuS(partial[s2,s1],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target_mu, target_S return target_mu, target_S
def psi2(self,Z,mu,S): def psi2(self,Z,mu,S,slices1=None,slices2=None):
""" """
:Z: np.ndarray of inducing inputs (M x Q) :Z: np.ndarray of inducing inputs (M x Q)
: mu, S: np.ndarrays of means and variacnes (each N x Q) : mu, S: np.ndarrays of means and variacnes (each N x Q)
:returns psi2: np.ndarray (N,M,M,Q) """ :returns psi2: np.ndarray (N,M,M,Q) """
target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[0])) target = np.zeros((Z.shape[0],Z.shape[0]))
[p.psi2(Z,mu,S,target=target) for p in self.parts] slices1, slices2 = self._process_slices(slices1,slices2)
[p.psi2(Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,s2]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target return target
def dpsi2_dtheta(self,Z,mu,S): def dpsi2_dtheta(self,partial,Z,mu,S,slices1=None,slices2=None):
"""Returns shape (N,M,M,Ntheta)""" """Returns shape (N,M,M,Ntheta)"""
target = np.zeros((Z.shape[0],Z.shape[0],self.Nparam)) slices1, slices2 = self._process_slices(slices1,slices2)
[p.dpsi2_dtheta(Z,mu,S,target[:,:,s]) for p,s in zip(self.parts, self.param_slices)] target = np.zeros(self.Nparam)
[p.dpsi2_dtheta(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[ps]) for p,i_s,s1,s2,ps in zip(self.parts,self.input_slices,slices1,slices2,self.param_slices)]
return target return target
def dpsi2_dZ(self,Z,mu,S): def dpsi2_dZ(self,partial,Z,mu,S,slices1=None,slices2=None):
"""N,M,M,Q""" slices1, slices2 = self._process_slices(slices1,slices2)
target = np.zeros((mu.shape[0],Z.shape[0],Z.shape[0],Z.shape[1])) target = np.zeros_like(Z)
[p.dpsi2_dZ(Z,mu,S,target) for p in self.parts] [p.dpsi2_dZ(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target[s2,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
return target return target
def dpsi2_dmuS(self,Z,mu,S): def dpsi2_dmuS(self,Z,mu,S,slices1=None,slices2=None):
"""return shapes are N,M,M,Q""" """return shapes are N,M,M,Q"""
target_mu, target_S = np.zeros((2,mu.shape[0],Z.shape[0],Z.shape[0],Z.shape[1])) slices1, slices2 = self._process_slices(slices1,slices2)
[p.dpsi2_dmuS(Z,mu,S,target_mu=target_mu,target_S = target_S) for p in self.parts] target_mu, target_S = np.zeros((2,mu.shape[0],mu.shape[1]))
[p.dpsi2_dmuS(partial[s2,s2],Z[s2,i_s],mu[s1,i_s],S[s1,i_s],target_mu[s1,i_s],target_S[s1,i_s]) for p,i_s,s1,s2 in zip(self.parts,self.input_slices,slices1,slices2)]
#TODO: there are some extra terms to compute here! #TODO: there are some extra terms to compute here!
return target_mu, target_S return target_mu, target_S

138
GPy/kern/rbf.py
View file

@ -17,7 +17,7 @@ class rbf(kernpart):
:param lengthscale: the lengthscale of the kernel :param lengthscale: the lengthscale of the kernel
:type lengthscale: float :type lengthscale: float
.. Note: for rbf with different lengthscales on each dimension, see rbf_ARD .. Note: for rbf with different lengthscale on each dimension, see rbf_ARD
""" """
def __init__(self,D,variance=1.,lengthscale=1.): def __init__(self,D,variance=1.,lengthscale=1.):
@ -81,80 +81,88 @@ class rbf(kernpart):
self._K_exponent = -0.5*self._K_dist2 self._K_exponent = -0.5*self._K_dist2
self._K_dvar = np.exp(-0.5*self._K_dist2) self._K_dvar = np.exp(-0.5*self._K_dist2)
if __name__=='__main__': def psi0(self,Z,mu,S,target):
#run some simple tests on the kernel (TODO:move these to unititest) target += self.variance
#TODO: these are broken in this new structure!
N = 10
M = 5
Q = 3
Z = np.random.randn(M,Q) def dpsi0_dtheta(self,partial,Z,mu,S,target):
mu = np.random.randn(N,Q) target[0] += 1.
S = np.random.rand(N,Q)
var = 2.5 def dpsi0_dmuS(self,Z,mu,S,target_mu,target_S):
lengthscales = np.ones(Q)*0.7 pass
k = rbf(Q,var,lengthscales) def psi1(self,Z,mu,S,target):
self._psi_computations(Z,mu,S)
target += self._psi1
from checkgrad import checkgrad def dpsi1_dtheta(self,partial,Z,mu,S,target):
self._psi_computations(Z,mu,S)
denom_deriv = S[:,None,:]/(self.lengthscale**3+self.lengthscale*S[:,None,:])
d_length = self._psi1[:,:,None]*(self.lengthscale*np.square(self._psi1_dist/(self.lengthscale2+S[:,None,:])) + denom_deriv)
target[0] += np.sum(partial*self._psi1/self.variance)
target[1] += np.sum(d_length*partial[:,:,None])
def k_theta_test(param,k): def dpsi1_dZ(self,partial,Z,mu,S,target):
k.set_param(param) self._psi_computations(Z,mu,S)
K = k.K(Z) target += np.sum(partial[:,:,None]*-self._psi1[:,:,None]*self._psi1_dist/self.lengthscale2/self._psi1_denom,0)
dK_dtheta = k.dK_dtheta(Z)
f = np.sum(K)
df = dK_dtheta.sum(0).sum(0)
return f,np.array(df)
print "dk_dtheta_test"
checkgrad(k_theta_test,np.random.randn(1+Q),args=(k,))
def dpsi1_dmuS(self,partial,Z,mu,S,target_mu,target_S):
self._psi_computations(Z,mu,S)
tmp = self._psi1[:,:,None]/self.lengthscale2/self._psi1_denom
target_mu += np.sum(partial*tmp*self._psi1_dist,1)
target_S += np.sum(partial*0.5*tmp*(self._psi1_dist_sq-1),1)
def psi1_mu_test(mu,k): def psi2(self,Z,mu,S,target):
mu = mu.reshape(N,Q) self._psi_computations(Z,mu,S)
f = np.sum(k.psi1(Z,mu,S)) target += self._psi2.sum(0) #TODO: psi2 should be NxMxM (for het. noise)
df = k.dpsi1_dmuS(Z,mu,S)[0].sum(1)
return f,df.flatten()
print "psi1_mu_test"
checkgrad(psi1_mu_test,np.random.randn(N*Q),args=(k,))
def psi1_S_test(S,k): def dpsi2_dtheta(self,partial,Z,mu,S,target):
S = S.reshape(N,Q) """Shape N,M,M,Ntheta"""
f = np.sum(k.psi1(Z,mu,S)) self._psi_computations(Z,mu,S)
df = k.dpsi1_dmuS(Z,mu,S)[1].sum(1) d_var = np.sum(2.*self._psi2/self.variance,0)
return f,df.flatten() d_length = self._psi2[:,:,:,None]*(0.5*self._psi2_Zdist_sq*self._psi2_denom + 2.*self._psi2_mudist_sq + 2.*S[:,None,None,:]/self.lengthscale2)/(self.lengthscale*self._psi2_denom)
print "psi1_S_test" d_length = d_length.sum(0)
checkgrad(psi1_S_test,np.random.rand(N*Q),args=(k,)) target[0] += np.sum(partial*d_var)
target[1] += np.sum(d_length*partial)
def psi1_theta_test(theta,k): def dpsi2_dZ(self,partial,Z,mu,S,target):
k.set_param(theta) """Returns shape N,M,M,Q"""
f = np.sum(k.psi1(Z,mu,S)) self._psi_computations(Z,mu,S)
df = np.array([np.sum(grad) for grad in k.dpsi1_dtheta(Z,mu,S)]) dZ = self._psi2[:,:,:,None]/self.lengthscale2*(-0.5*self._psi2_Zdist + self._psi2_mudist/self._psi2_denom)
return f,df target += np.sum(partial[None,:,:,None]*dZ,0).sum(1)
print "psi1_theta_test"
checkgrad(psi1_theta_test,np.random.rand(1+Q),args=(k,))
def dpsi2_dmuS(self,Z,mu,S,target_mu,target_S):
"""Think N,M,M,Q """
self._psi_computations(Z,mu,S)
tmp = self._psi2[:,:,:,None]/self.lengthscale2/self._psi2_denom
target_mu += (partial*-tmp*2.*self._psi2_mudist).sum(1).sum(1)
target_S += (partial*tmp*(2.*self._psi2_mudist_sq-1)).sum(1).sum(1)
def psi2_mu_test(mu,k): def _psi_computations(self,Z,mu,S):
mu = mu.reshape(N,Q) #here are the "statistics" for psi1 and psi2
f = np.sum(k.psi2(Z,mu,S)) if not np.all(Z==self._Z):
df = k.dpsi2_dmuS(Z,mu,S)[0].sum(1).sum(1) #Z has changed, compute Z specific stuff
return f,df.flatten() self._psi2_Zhat = 0.5*(Z[:,None,:] +Z[None,:,:]) # M,M,Q
print "psi2_mu_test" self._psi2_Zdist = Z[:,None,:]-Z[None,:,:] # M,M,Q
checkgrad(psi2_mu_test,np.random.randn(N*Q),args=(k,)) self._psi2_Zdist_sq = np.square(self._psi2_Zdist)/self.lengthscale2 # M,M,Q
self._Z = Z
def psi2_S_test(S,k): if not (np.all(Z==self._Z) and np.all(mu==self._mu) and np.all(S==self._S)):
S = S.reshape(N,Q) #something's changed. recompute EVERYTHING
f = np.sum(k.psi2(Z,mu,S))
df = k.dpsi2_dmuS(Z,mu,S)[1].sum(1).sum(1) #TODO: make more efficient for large Q (using NDL's dot product trick)
return f,df.flatten() #psi1
print "psi2_S_test" self._psi1_denom = S[:,None,:]/self.lengthscale2 + 1.
checkgrad(psi2_S_test,np.random.rand(N*Q),args=(k,)) self._psi1_dist = Z[None,:,:]-mu[:,None,:]
self._psi1_dist_sq = np.square(self._psi1_dist)/self.lengthscale2/self._psi1_denom
self._psi1_exponent = -0.5*np.sum(self._psi1_dist_sq+np.log(self._psi1_denom),-1)
self._psi1 = self.variance*np.exp(self._psi1_exponent)
#psi2
self._psi2_denom = 2.*S[:,None,None,:]/self.lengthscale2+1. # N,M,M,Q
self._psi2_mudist = mu[:,None,None,:]-self._psi2_Zhat #N,M,M,Q
self._psi2_mudist_sq = np.square(self._psi2_mudist)/(self.lengthscale2*self._psi2_denom)
self._psi2_exponent = np.sum(-self._psi2_Zdist_sq/4. -self._psi2_mudist_sq -0.5*np.log(self._psi2_denom),-1) #N,M,M
self._psi2 = np.square(self.variance)*np.exp(self._psi2_exponent) # N,M,M
self._Z, self._mu, self._S = Z, mu,S
def psi2_theta_test(theta,k):
k.set_param(theta)
f = np.sum(k.psi2(Z,mu,S))
df = np.array([np.sum(grad) for grad in k.dpsi2_dtheta(Z,mu,S)])
return f,df
print "psi2_theta_test"
checkgrad(psi2_theta_test,np.random.rand(1+Q),args=(k,))

18
GPy/kern/white.py
View file

@ -54,33 +54,33 @@ class white(kernpart):
def psi0(self,Z,mu,S,target): def psi0(self,Z,mu,S,target):
target += self.variance target += self.variance
def dpsi0_dtheta(self,Z,mu,S,target): def dpsi0_dtheta(self,partial,Z,mu,S,target):
target += 1. target += partial.sum()
def dpsi0_dmuS(self,Z,mu,S,target_mu,target_S): def dpsi0_dmuS(self,partial,Z,mu,S,target_mu,target_S):
pass pass
def psi1(self,Z,mu,S,target): def psi1(self,Z,mu,S,target):
pass pass
def dpsi1_dtheta(self,Z,mu,S,target): def dpsi1_dtheta(self,partial,Z,mu,S,target):
pass pass
def dpsi1_dZ(self,Z,mu,S,target): def dpsi1_dZ(self,partial,Z,mu,S,target):
pass pass
def dpsi1_dmuS(self,Z,mu,S,target_mu,target_S): def dpsi1_dmuS(self,partial,Z,mu,S,target_mu,target_S):
pass pass
def psi2(self,Z,mu,S,target): def psi2(self,Z,mu,S,target):
pass pass
def dpsi2_dZ(self,Z,mu,S,target): def dpsi2_dZ(self,partial,Z,mu,S,target):
pass pass
def dpsi2_dtheta(self,Z,mu,S,target): def dpsi2_dtheta(self,partial,Z,mu,S,target):
pass pass
def dpsi2_dmuS(self,Z,mu,S,target_mu,target_S): def dpsi2_dmuS(self,partial,Z,mu,S,target_mu,target_S):
pass pass

1
GPy/models/__init__.py
View file

@ -9,3 +9,4 @@ from warped_GP import warpedGP
from GP_EP import GP_EP from GP_EP import GP_EP
from generalized_FITC import generalized_FITC from generalized_FITC import generalized_FITC
from sparse_GPLVM import sparse_GPLVM from sparse_GPLVM import sparse_GPLVM
from uncertain_input_GP_regression import uncertain_input_GP_regression

17
GPy/models/uncertain_input_GP_regression.py
View file

@ -7,7 +7,7 @@ from ..util.linalg import mdot, jitchol, chol_inv, pdinv
from ..util.plot import gpplot from ..util.plot import gpplot
from .. import kern from .. import kern
from ..inference.likelihoods import likelihood from ..inference.likelihoods import likelihood
from GP_regression import GP_regression from sparse_GP_regression import sparse_GP_regression
class uncertain_input_GP_regression(sparse_GP_regression): class uncertain_input_GP_regression(sparse_GP_regression):
""" """
@ -33,6 +33,7 @@ class uncertain_input_GP_regression(sparse_GP_regression):
""" """
def __init__(self,X,Y,X_uncertainty,kernel=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False): def __init__(self,X,Y,X_uncertainty,kernel=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
self.X_uncertainty = X_uncertainty
sparse_GP_regression.__init__(self, X, Y, kernel = kernel, beta = beta, normalize_X = normalize_X, normalize_Y = normalize_Y) sparse_GP_regression.__init__(self, X, Y, kernel = kernel, beta = beta, normalize_X = normalize_X, normalize_Y = normalize_Y)
self.trYYT = np.sum(np.square(self.Y)) self.trYYT = np.sum(np.square(self.Y))
@ -40,22 +41,22 @@ class uncertain_input_GP_regression(sparse_GP_regression):
# kernel computations, using BGPLVM notation # kernel computations, using BGPLVM notation
#TODO: slices for psi statistics (easy enough) #TODO: slices for psi statistics (easy enough)
self.Kmm = self.kern.K(self.Z) self.Kmm = self.kern.K(self.Z)
self.psi0 = self.kern.psi0(self.X,slices=self.Xslices).sum() self.psi0 = self.kern.psi0(self.Z,self.X, self.X_uncertainty).sum()
self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty) self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty).T
self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty) self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
def dL_dtheta(self): def dL_dtheta(self):
#re-cast computations in psi2 back to psi1: #re-cast computations in psi2 back to psi1:
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z) dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_uncertainty) dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_uncertainty)
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1,self.Z,self.X, self.X_uncertainty) dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_uncertainty)
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty) dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty) # for multiple_beta, dL_dpsi2 will be a different shape
return dL_dtheta return dL_dtheta
def dL_dZ(self): def dL_dZ(self):
dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm,self.Z,)#factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm,self.Z,)#factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
dL_dZ += self.kern.dpsi1_dZ(dL_dpsi1,self.Z,self.X, self.X_uncertainty) dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1.T,self.Z,self.X, self.X_uncertainty)
dL_dZ += self.kern.dpsi2_dZ(dL_dpsi2,self.Z,self.X, self.X_uncertainty) dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty)
return dL_dZ return dL_dZ
def plot(self,*args,**kwargs): def plot(self,*args,**kwargs):
@ -65,5 +66,5 @@ class uncertain_input_GP_regression(sparse_GP_regression):
""" """
sparse_GP_regression.plot(self,*args,**kwargs) sparse_GP_regression.plot(self,*args,**kwargs)
if self.Q==1: if self.Q==1:
pb.errorbar(self.X[:,0], pb.ylim(0) ,xerr=2*np.sqrt(self.X_uncertainty.flatten())) pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten()))